Online classification of unstructured free-living exercise sessions in people with Type 1 Diabetes

Background: Managing exercise in type 1 diabetes (T1D) is challenging, in part because different types of exercise can have diverging effects on glycemia. The aim of this work was to develop a classification model that can classify an exercise event (structured or unstructured) as aerobic, interval or resistance for the purpose of incorporation into an automated insulin delivery (AID) system. Methods: A long short-term memory (LSTM) network model was developed with real world data from 30-minute structured sessions of at-home exercise (aerobic, resistance, or mixed) using tri-axial accelerometer, heart rate and activity duration information. The detection algorithm was used to classify 15 common free-living and unstructured activities and relate each to exercise-associated change in glucose. Results: A total of 1610 structured exercise sessions were used to train, validate and test the model. The accuracy for the structured exercise sessions in the testing set was 72% for aerobic, 65% for interval and 77% for resistance. Additionally, we tested the classifier on 3328 unstructured sessions. We validated the session-associated change in glucose against the expected change during exercise for each type. Mean and standard deviation of the change in glucose of -20.8 (40.3) mg/dl was achieved for sessions classified as aerobic, -16.2 (39.0) mg/dl for sessions classified as interval and -11.6 (38.8) mg/dl for sessions classified as resistance. Conclusions: The proposed algorithm reliably identified physical activity associated with expected change in glucose that could be integrated into an AID system to manage the exercise disturbance in glycemia according to the predicted class.